Introduction
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Welcome to my portfolio,
I’m Reginald van Putt and this is my portfolio for the course Computational Musicology (UvA). In this portfolio I will investigate a corpus that I have created with help of my peers and friends. The corpus exists off the top 5 songs of people from all kinds of bachelors. With this corpus I’m trying to investigate whether or not there is a significant difference in study directions in terms of music taste. Specifically looking at the valence, energy and genre of songs.
Why?
The reason why I wanted to investigate this is because I quite often ask people about their music taste and I have done a few courses from different faculties and it seemed to me that different faculties (i.e. FNWI vs AI) have quite different tastes in music. So I that’s why I wanted to analyse more data and see if there is a significant difference.
Visualizations
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Split visualisation
This is a temporary graph that shows all the data I have incorperated in my corpus untill now. I have more data to incorperate (but this takes a lot of time) and I keep receiving more data through google forms and the canvas discussions. For now this is the layout and format I have chosen to use. On the x-axis you can see the valence and on the y-axis the energy of the songs. The songs plotted are the top 5 songs of many peers and friends, the colors are divided based on their bachelor and the graphs are divided based on faculty/direction of the bachelor. This might change in the future but I still have not decided how to split all bachelors/data.
Statistical analysis
$statistics
MSerror Df Mean CV
0.05274719 126 0.4876946 47.0925
$parameters
test name.t ntr StudentizedRange alpha
Tukey faculty 4 3.682115 0.05
$means
valence std r se Min Max
Faculty of Humanities 0.3325633 0.2139572 30 0.04193137 0.0376 0.915
Faculty of Science 0.5602222 0.2265838 45 0.03423682 0.1590 0.929
Social and Behavior studies 0.4976467 0.2548524 30 0.04193137 0.0994 0.875
Technical University studies 0.5313600 0.2213671 25 0.04593351 0.1280 0.912
Q25 Q50 Q75
Faculty of Humanities 0.187 0.2740 0.41900
Faculty of Science 0.350 0.5800 0.74100
Social and Behavior studies 0.275 0.4825 0.74075
Technical University studies 0.299 0.5660 0.68600
$comparison
NULL
$groups
valence groups
Faculty of Science 0.5602222 a
Technical University studies 0.5313600 a
Social and Behavior studies 0.4976467 a
Faculty of Humanities 0.3325633 b
attr(,"class")
[1] "group"Chromagram
The reason that the left song has lower energy and valence might be due to the lower frequency of notes, it looks like the amount of notes is quite a bit lower then in the right song / average song. Secondly the valence might be lower because of the fact that the outlier song is mostly minor while the average song is mostly major (according to spotify). Which you might be able to see looking at the chords played througout the song.
P.S. I know that the graphs are not of the same size, I have spend an hour and a half trying to fix that and I could not do it. I’m not planning on using chromagrams in my final portfolio if not manditory so I did not want to spend more time then 2 hours on fixing the sizes of graphs.
Key analysis
Low
High
Random
The keygrams are quit interesting to compare. The lowest valence song shows a vertical band between 150 and 200 seconds. Meaning that there are more keys being used in that time period compared to the rest. Throughout the song the keys that are used the majority of the time are kind of the same. but the quantity/certainty of it differs a bit. For the highest valence song it’s quite different. there are not a lot of keys that are used but the keys that are used are really stable though the whole song. For the random song there is a lot of uniformity throught the song, but some slight differences such as in the intro and 2 (probably) bridges of sort.
Chord analysis
Low
High
Random
There seem to be a very big overlap between chords used in all three songs. Almost (if not all) chords seem to be the same (if looking at the blue-est parts of the chordgram). This surprised me since the valence of these songs is quite different and not all songs are of the same genre (so not just generic pop songs).
Tempo analysis
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Novelty graph of loudness
Low valence
High valence
Here you see two songs of the corpus, more specifically two songs out of the Faculty of Science category. The first graph is a novelty graph of loudness of the song with the lowest valence and the second graph is a novelty graph of loudness of the song with the highest valence (both according to spotify’s api). ### Tempogram
Low valence
High valence
Dendrogram
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Not done yet, welp (it has been around 6 hours already :’)
Explanation of the graph
I have not got this graph to work yet, i was planning on showing multiple faculties seperated by colour to see how things overlap and are in common but I keep getting crashes and colours not working properly. So for now I only have this for the science faculties. A thing to note is that there are the same amount of clusters then people in this group, which could mean that they all have quite different preferences in songs. However, for some reason in the last code push the song labels have dissapeared and I have not yet found a fix.
Discussion/Conclusion
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I have not yet had the time to do a statistic analysis (and I have also not added all the data yet) so I have no clear conclusion yet. For now with the naked eye it seems like there is no significant difference between different groups (excluding the Faculty of Medicine because of the low sample size).